IBM SPSS Statistics 19 Guide to Data Analysis: International Edition

The PASW Statistics 19 Guide to Data Analysis is a friendly introduction to both data analysis and PASW Statistics 19 (formerly SPSS Statistics), the world’s leading desktop statistical software package. Easy-to-understand explanations and in-depth content make this guide both an excellent supplement to other statistics texts and a superb primary text for any introductory data analysis course. With this book, you’ll learn how to describe data, test hypotheses, and examine relationships using PASW. Author Marija Norušis incorporates a wealth of real data, including the General Social Survey and studies of Internet usage, opinions of the criminal justice system, marathon running times, library patronage, and the importance of manners, throughout the examples and expanded chapter exercises. This unique combination of examples, exercises, and contemporary data gives you hands-on experience in analyzing data and makes learning about data analysis and statistical software relevant, unintimidating, and even fun! A data CD-ROM is included with this book.

Table of Contents:
PART 1. GETTING STARTED WITH IBM SPSS STATISTICS 1. Introduction About This Book Getting Started with IBM SPSS Statistics             Describing Data             Testing Hypotheses             Examining Relationships             Lets Get Started   2. An Introductory Tour of IBM SPSS Statistics Starting IBM SPSS Statistics             Help Is Always at Hand Copying the Data Files Opening a Data File Statistical Procedures             The Viewer Window             Viewer Objects The Data Editor Window             Entering Non-Numeric Data             Clearing the Data Editor without Saving Changes The IBM SPSS Statistics Online Tutorial The IBM SPSS Statistics Toolbar The IBM SPSS Statistics Help System             Contextual Help What’s Next?   3. Sources of Data Know Your Data Survey Data             Asking the Question             Measuring Time             Selecting Participants             Selecting a Sample             General Social Survey             Random-Digit Dialing             Internet Surveys Designing Experiments             Random Assignment             Minimizing Bias Summary What’s Next? Exercises   PART 2. DESCRIBING DATA 4. Counting Responses Describing Variables             A Simple Frequency Table             Sorting Frequency Tables             Pie Charts             Bar Charts Summarizing Internet Time             Histograms             Mode and Median             Percentiles Summary What’s Next? How to Obtain a Frequency Table             Format: Appearance of the Frequency Table             Statistics: Univariate Statistics             Charts: Bar Charts, Pie Charts, and Histograms Exercises   5. Computing Descriptive Statistics Summarizing Data             Scales of Measurement             Mode, Median, and Arithmetic Average             Comparing Mean and Median Summarizing Time Spent Online Measures of Variability             Range             Variance and Standard Deviation             The Coefficient of Variation Standard Scores Summary What’s Next? How to Obtain Univariate Descriptive Statistics             Options: Choosing Statistics and Sorting Variables Exercises   6. Comparing Groups Age, Education, and Internet Use             Plotting Means             Layers: Defining Subgroups by More than One Variable Summary What’s Next? How to Obtain Subgroup Means             Layers: Defining Subgroups by More than One Variable             Options: Additional Statistics and Display of Labels Exercises   7. Looking at Distributions Marathon Completion Times             Age and Gender             Marathon Times for Mature Runners Summary What’s Next? How to Explore Distributions             Explore Statistics             Graphical Displays             Options Exercises   8. Counting Responses for Combinations of Variables Library Use and Education             Row and Column Percentages             Bar Charts             Adding Control Variables             Library Use and the Internet Summary What’s Next? How to Obtain a Crosstabulation             Layers: Three or More Variables at Once             Cells: Percentages, Expected Counts, and Residuals             Bivariate Statistics             Format: Adjusting the Table Format Exercises   9. Plotting Data Examining Population Indicators             Simple Scatterplots             Scatterplot Matrices             Overlay Plots             Three-Dimensional Plots             Identifying Unusual Points             Rotating 3-D Scatterplots Summary What’s Next? How to Obtain a Scatterplot             Obtaining a Simple Scatterplot             Obtaining an Overlay Scatterplot             Obtaining a Scatterplot Matrix             Obtaining a 3-D Scatterplot             Editing a Scatterplot Exercises   PART 3. TESTING HYPOTHESES 10. Evaluating Results from Samples From Sample to Population             A Computer Model             The Effect of Sample Size             The Binomial Test Summary What’s Next? Exercises   11. The Normal Distribution The Normal Distribution             Samples from a Normal Distribution             Means from a Normal Population             Are the Sample Results Unlikely?             Testing a Hypothesis             Means from Non-Normal Distributions             Means from a Uniform Distribution Summary What's Next? Exercises   12. Testing a Hypothesis about a Single Mean Examining the Data The T Distribution             Calculating the T Statistic Confidence Intervals             Other Confidence Levels             Confidence Interval for a Difference             Confidence Intervals and Hypothesis Tests Null Hypotheses and Alternative Hypotheses             Rejecting the Null Hypothesis Summary What's Next? How to Obtain a One-Sample T Test             Options: Confidence Level and Missing Data Exercises   13. Testing a Hypothesis about Two Related Means Marathon Runners in Paired Designs             Looking at Differences             Is the Mean Difference Zero?             Two Approaches The Paired-Samples T Test             Are You Positive?             Some Possible Problems             Examining Normality Summary What's Next? How to Obtain a Paired-Samples T Test             Options: Confidence Level and Missing Data Exercises   14. Testing a Hypothesis about Two Independent Means Examining Television Viewing             Distribution of Differences             Standard Error of the Mean Difference             Computing the T Statistic             Output from the Two-Independent-Samples T Test             Confidence Intervals for the Mean Difference             Testing the Equality of Variances Effect of Outliers Introducing Education             Can You Prove the Null Hypothesis?             Interpreting the Observed Significance Level             Power             Monitoring Death Rates             Does Significant Mean Important? Summary What's Next? How to Obtain an Independent-Samples T Test             Define Groups: Specifying the Subgroups             Options: Confidence Level and Missing Data Exercises   15. One-Way Analysis of Variance Hours in a Work Week             Describing the Data             Confidence Intervals for the Group Means             Testing the Null Hypothesis             Assumptions Needed for Analysis of Variance             Analyzing the Variability             Comparing the Two Estimates of Variability             The Analysis-of-Variance Table Multiple Comparison Procedures             Television Viewing, Education, and Internet Use Summary What's Next? How to Obtain a One-Way Analysis of Variance             Post Hoc Multiple Comparisons: Finding the Difference             Options: Statistics and Missing Data Exercises   16. Two-Way Analysis of Variance The Design             Examining the Data             Testing Hypotheses             Degree and Gender Interaction             Necessary Assumptions             Analysis-of-Variance Table             Testing the Degree-by-Gender Interaction             Testing the Main Effects             Removing the Interaction Effect             Where Are the Differences? Multiple Comparison Results             Checking Assumptions A Look at Television Extensions Summary What's Next? How to Obtain a GLM Univariate Analysis             GLM Univariate: Model             GLM Univariate: Plots             GLM Univariate: Post Hoc             GLM Univariate: Options             GLM Univariate: Save Exercises   17. Comparing Observed and Expected Counts Freedom or Manners?             Observed and Expected Counts             The Chi-Square Statistic             A Larger Table Does College Open Doors? A One-Sample Chi-Square Test Power Concerns Summary What's Next? Exercises   18. Nonparametric Tests Nonparametric Tests for Paired Data             Sign Test             Wilcoxon Test             Who's Sending E-mail? Mann-Whitney Test Kruskal-Wallis Test Friedman Test Summary How to Obtain Nonparametric Tests             Chi-Square Test             Binomial Test             Two-Independent-Samples Tests             Several-Independent-Samples Tests             Two-Related-Samples Tests             Several-Related-Samples Tests             Options: Descriptive Statistics and Missing Values Exercises   PART 4. EXAMINING RELATIONSHIPS 19. Measuring Association Components of the Justice System Proportional Reduction in Error Measures of Association for Ordinal Variables             Concordant and Discordant Pairs             Measures Based on Concordant and Discordant Pairs             Evaluating the Components             Measuring Agreement             Correlation-Based Measures Measures Based on the Chi-Square Statistic Summary What's Next? Exercises   20. Linear Regression and Correlation Life Expectancy and Birthrate             Choosing the Best Line Calculating the Least-Squares Line             Calculating Predicted Values and Residuals             Determining How Well the Line Fits             Explaining Variability             Some Warnings Summary What's Next? How to Obtain a Linear Regression             Statistics: Further Information on the Model             Residual Plots: Basic Residual Analysis             Linear Regression Save: Creating New Variables             Linear Regression Options Exercises   21. Testing Regression Hypotheses The Population Regression Line             Assumptions Needed for Testing Hypotheses Testing Hypotheses             Testing that the Slope Is Zero             Confidence Intervals for the Slope and Intercept Predicting Life Expectancy             Predicting Means and Individual Observations             Standard Error of the Predicted Mean             Confidence Intervals for the Predicted Means             Prediction Intervals for Individual Cases Summary What's Next? How to Obtain a Bivariate Correlation             Options: Additional Statistics and Missing Data How to Obtain a Partial Correlation             Options: Additional Statistics and Missing Data Exercises   22. Analyzing Residuals Residuals             Standardized Residuals             Studentized Residuals             Checking for Normality             Checking for Constant Variance             Checking Linearity Checking Independence A Final Comment on Assumptions Looking for Influential Points             Studentized Deleted Residuals Summary What's Next? Exercises   23. Building Multiple Regression Models Predicting Life Expectancy             The Model             Assumptions for Multiple Regression             Examining the Variables             Looking at How Well the Model Fits             Examining the Coefficients             Interpreting the Partial Regression Coefficients             Changing the Model             Partial Correlation Coefficients             Tolerance and Multicollinearity             Beta Coefficients Building a Regression Model             Methods for Selecting Variables Summary What's Next? How to Obtain a Multiple Linear Regression             Options: Variable Selection Criteria Exercises   24. Multiple Regression Diagnostics Examining Normality Scatterplots of Residuals Leverage Changes in the Coefficients Cook's Distance Plots against Independent Variables             Partial Regression Plot Why Bother? Summary Exercises   Appendices A. Obtaining Charts in IBM SPSS Statistics Overview Creating Bar Charts             Creating a Bar Chart for Single Variable             Creating a Clustered Bar Chart             Creating a Chart with Multiple Variables Modifying Charts             Collapsing Pie Chart Slices             Changing the Scale of Histogram             Saving Chart Files B. Transforming and Selecting Data Data Transformations             Transformations at a Glance             Saving Changes             Delaying Processing of Transformations             Recoding Values Computing Variables             The Calculator Pad             Automatic Recoding             Conditional Transformations Case Selection             Temporary or Permanent Selection             Other Selection Methods C. The T Distribution D. Areas under the Normal Curve E. Descriptions of Data Files F. Answers to Selected Exercises